Simultaneous Reconstruction and Moving Object Detection From Compressive Sampled Surveillance Videos

The spatially distributed digital cameras in Wireless Multimedia Sensor Networks (WMSN) are provided with miniature batteries resulting in power constraints. These cameras acquire compressive measurements of video at a rate significantly below the Nyquist rate and transmit them wirelessly in the network. Thus, the encoding side (transmitter) is made less complex at the expense of increased complexity at the more resourceful decoder (receiver). Well grounded on this relevant practical scenario, a unified system is proposed that integrates detection of moving objects into the Compressive Sensing (CS) recovery framework and thereby realizing simultaneous data recovery and object detection in a single optimization problem which guarantees fast response. In this work, a new tensor RPCA approach is proposed to accomplish this requirement. For background separation, low rank approximation is done on the highly correlated background components. A Laplace function based surrogate for tensor tubal rank is formulated to provide adaptive thresholding for the singular value tubes of the background tensor. Moreover, the spatio-temporal continuity of the foreground is explored using 3D-Piecewise Smoothness Constraints combinations based Anisotropic Total Variation (3D-PSCATV) regularization. Additionally, $l_{1}$ regularization has been adopted to describe the sparsity of moving objects. The proposed model is solved using Alternative Direction Method of Multipliers (ADMM) scheme. The quantitative and qualitative results validate the superior performance of the proposed method against the compared approaches.